91影视

Dr. Samuel Gyamerah is an Assistant Professor at 91影视 whose work sits at the intersection of AI, data science, statistics, and quantitative modeling. His research and professional experience span climate risk, insurance analytics, financial mathematics, systemic risk, machine learning, predictive modeling, and decision support.

He develops mathematically rigorous, data-driven methods for complex real-world problems, with applications in weather index insurance, financial and insurance risk, credit and systemic risk, health analytics, forecasting, and uncertainty quantification. His work combines machine learning, stochastic modeling, optimization, and statistical analysis to generate actionable insight for academia, policy, and industry. 

His research spans climate,agriculture, insurance, finance, health informatics, and emerging quantum methods, combining statistical modeling, optimization, and machine learning. His work includes probabilistic crop-yield forecasting with interval prediction and applied clinical risk prediction using machine learning. In insurance analytics, he develops methods for highly imbalanced fraud detection with explainability and robust evaluation. He also contributes to LLM-enabled symptom extraction with compression-strategy evaluation, optimizing index-insurance strike parameters to reduce basis risk, robust multi-factor climate-risk pricing, training-stability advances for variational quantum circuits, and single-cell perturbation modeling using advanced learning and optimization frameworks. 

Dr. Gyamerah has a strong record of peer-reviewed research, grant success, interdisciplinary collaboration, postgraduate supervision, and curriculum development. He has led and contributed to projects in climate finance, actuarial analytics, predictive health modeling, and AI-enabled risk analytics. He has also taught and mentored across statistics, data science, financial mathematics, and quantitative computing.

With technical proficiency in Python, R, SQL, SAS, SPSS, Stata, forecasting, simulation, and data visualization, Dr. Gyamerah is interested in collaborations and opportunities in AI and machine learning research, quantitative analytics, climate and financial risk, insurance innovation, and applied data science. His broader goal is to translate advanced quantitative methods into practical tools that improve decision-making under uncertainty.